Tapio Niemi

Constructing OLAP cubes based on queries

An On-Line Analytical Processing (OLAP) user often follows a train of thought, posing a sequence of related queries against the data warehouse. Although their details are not known in advance, the general form of those queries is apparent beforehand. Thus, the user can outline the relevant portion of the data posing generalised queries against a cube representing the data warehouse.Since existing OLAP design methods are not suitable for non-professionals, we present a technique that automates cube design given the data warehouse, functional dependency information, and sample OLAP queries expressed in the general form. The method constructs complete but minimal cubes with low risks related to sparsity and incorrect aggregations. After the user has given queries, the system will suggest a cube design. The user can accept it or improve it by giving more queries. The method is also suitable for improving existing cubes using respective real MDX queries.

Normalising OLAP cubes for controlling sparsity

A poorly designed OLAP (on-line analytical processing) cube can have a size much larger than the volume of information, potentially leading to problems with performance and usability. We give a new normal form for OLAP cube design and synthesis and decomposition algorithms to produce normalised OLAP cube schemata. OLAP cube normalisation controls the structural sparsity resulting from inter-dimensional functional dependencies. We assume that functional dependencies are used to describe the constraints of the application universe of discourse. Our methods help the user to identify cube schemata with structural sparsity, and to change the design in order to obtain more economy of space.

Nanoimprint Lithography - Next Generation Nanopatterning Methods for Nanophotonics Fabrication

Nanophotonics is wide field covering many interesting applications branching from cutting edge science including plasmonics, metamaterials, cavity quantum electrodynamics in high-Q cavities all the way to applied sciences like silicon nanophotonics for on chip optical interconnections and single frequency semiconductor light sources. Most of the practical device demonstrations in these fields utilize nanopatterned surfaces. Applications require patterning of nanoscopic gratings, photonic crystals, waveguides and metal structures. There are many wonderful demonstrations of nanotechnology-based lasers and other photonic components. However, difficult questions related to fabrication need to be addressed before these components enter any market. Demonstrations in the scientific literature have relied heavily on the use of direct writing lithography methods, such as electron beam lithography or focused ion beam lithography. These methods, although excellent for scientific studies, cannot be scaled up to allow cost effective production of nanophotonics. Lithography solutions developed for integrated circuits can produce extremely narrow linewidths and deliver high precision but are difficult to transfer to photonics fabrication. There exist many alternative lithography methods, but their scale-up to cost effective volume production is challenging. Since the introduction of nanoimprint lithography (NIL) in 1995 (Chou et al. 1995), there has been widespread interest in the development of NIL for various applications. As early as in 2003 NIL had gained substantial support and was chosen as one of MITs Technology Review’s “10 Emerging Technologies That Will Change the World” (Technology Review 2003). The selection was justified by the fact that NIL can bridge the gap between lab level nanotechnology research and production level manufacturing requirements. In this chapter, we briefly review the state-of-the-art lithography methods and introduce nanoimprint lithography (NIL), a very cost effective lithography method for nanophotonics applications. In section two we will introduce soft UV-NIL, an imprint method using soft and flexible stamps, as a method for patterning compound semiconductor optoelectronics. Finally, in section three, we highlight some NIL activities based on soft-UV-NIL.

Searching neural network structures with L systems and genetic algorithms

We present a new method for using genetic algorithms and L systems to grow up efficient neural network structures. Our L rules operate directly on 2-dimensional cell matrix. Lrules are produced automatically by genetic algorithm and they have“age”that controls the number of firing times, i.e., times we can apply each rule. We have modified the conventional neural network model so that it is easy to present the knowledge by birth (axon weights) and the learning by experience (dendrite weights). A connection is shown to exist between the axon weights and learning parameters used e.g., in back propagation. This system enables us to find special structures that are very fast for both to train and to operate comparing to conventional, layered methods.

Functional Dependencies in Controlling Sparsity of OLAP Cubes

We will study how relational dependency information can be applied to OLAP cube design. We use dependency information to control sparsity, since functional dependencies between dimensions clearly increase sparsity. Our method helps the user in finding dimensions and hierarchies, identifying sparsity risks, and finally changing the design in order to get a more suitable result. Sparse raw data, a large amount of pre-calculated aggregations, and many dimensions may expand the need of the storage space so rapidly that the problem cannot be solved by increasing the capacity of the system. We give two methods to construct suitable OLAP cubes. In the synthesis method, attributes are divided into equivalence classes according to dependencies in which they participate. Each equivalence class may form a dimension. The decomposition method is applied when candidates for dimensions exist. We decompose dimensions based on conflicts, and construct new cubes for removed dimensions until no conflicts between dimensions exist.

Moth eye antireflection coated GaInP/GaAs/GaInNAs solar cell

The performance of a GaInP/GaAs/GaInNAs solar cell incorporating AlInP moth eye antireflection coating is reported and compared with the performance of a similar cell comprising TiO2/SiO2 antireflection coating. The moth eye coating exhibits an average reflectance of only 2% within the spectral range from 400 nm to 1600 nm. EQE measurements revealed absorption-related losses in the AlInP moth eye coating at wavelengths below 510 nm. Short wavelength absorption decreases the current generation in the top GaInP junction by 10%. Despite the absorption losses, the moth eye patterned GaInP/GaAs/GaInNAs solar cell exhibited higher current generation under AM1.5G real sun illumination.